Optimizing TBI Inpatient Rehabilitation: Longitudinal Analysis of Intervention

项目总结 住院治疗创伤性脑损伤的康复在很大程度上被认为是未分化的黑人。 盒。这项前瞻性、观察性的基于实践证据(PBE)的研究详尽地记录了 数十万次治疗，一套丰富的纵向结果，以及无与伦比的 确定伤害严重程度和疾病共病的特征，以阐明这一护理的“黑匣子”。这个 迄今的分析表明，六项“高影响”活动在近几年中取得了更好的成果。 所有严重程度亚组和许多不同的结果。 拟议的项目旨在确定和描述特定的治疗应答者群体。 和无应答者在TBI-PBE数据库中，并确定与 更好的每周FIM和9个月的结果，控制患者的特征和治疗反应级别。 我们将使用生长混合建模(GMM)来识别响应者和非响应者类别。GMM 识别应答者和非应答者的方法比更传统的端点具有优势 治疗反应分析：与终点分析相比，它减少了偏差并改进了信号检测，因为 它解释了来自同一个人的测量与结果的不相等方差之间的相关性 随着时间的推移。我们将使用协方差分析(ANCOVA)线性混合效应回归模型进行比较 基线后的FIM测量每组干预措施的累积时间。作为二次分析， 我们将研究FIM在其他时间点的相关性，以确定早期治疗干预的特征。 这一拟议项目的完成将促进对住院康复护理的了解 经历过中、重度颅脑损伤，对康复有很高价值的人 在这个令人难以置信的动态恢复期进行治疗计划。该项目利用了可能的 是有史以来收集的关于脑损伤住院康复的最丰富的数据集，由于其起源于临床实践， 培养临床医生的高度接受性，并证明医疗保健系统的可行性 利益相关者。关于不同个体如何反应的更准确和详细的信息 干预措施将允许在急性康复期间和之后制定更多针对患者的治疗计划 出院。更好地了解干预的性质和时机将有助于 这是一种有效的治疗方式，将使有限的资源得到更有效的利用。此外，建议的 到目前为止，研究直接建立在研究团队的工作基础上，并有可能克服显著的 实现更知情、更有效和更公平的护理的障碍。

PROJECT SUMMARY Inpatient rehabilitation for traumatic brain injury has been studied largely as an undifferentiated black box. The prospective, observational TBI Practice-Based Evidence (PBE) study exhaustively documented hundreds of thousands of therapy sessions, a rich set of longitudinal outcomes, and an unrivaled characterization of injury severity and disease comorbidity in order to shed light on this “black box” of care. The analyses to date demonstrated that six “high impact” activities were associated with better outcomes in nearly all severity subgroups and across many different outcomes. The proposed project intends to identify and characterize specific populations of treatment responders and non-responders in the TBI-PBE database and to identify therapy interventions that are associated with better weekly FIM and 9-month outcomes, controlling for patient characteristics and treatment response class. We will use growth mixture modeling (GMM) to identify responder and non-responder classes. The GMM approach to identification of responders and non-responders has advantages over more traditional end-point analysis of treatment response: it reduces bias and improves signal detection over end-point analyses because it accounts for correlations between measures from the same individual and unequal variances of the outcome over time. We will use analysis of covariance (ANCOVA) linear mixed effect regression models to compare post-baseline FIM measures with cumulative time on each of the set of interventions. As a secondary analysis, we will look at FIM associations at other time points to characterize early treatment interventions. The completion of this proposed project will advance knowledge about inpatient rehabilitation care for persons who have experienced moderate and severe TBI and will be highly valuable for rehabilitation treatment planning during this incredibly dynamic period of recovery. The project makes use of what may likely be the richest data set on TBI inpatient rehabilitation ever assembled, which due to its origin in clinical practice, fosters high levels of acceptability from clinicians and demonstrates feasibility to health care system stakeholders. The more accurate and detailed information about how different individuals respond to interventions would allow more patient-specific treatment planning during acute rehabilitation and after discharge. Better understanding of the nature and timing of interventions would inform the staging of therapeutic modalities and would result in more effective use of limited resources. In addition, the proposed research builds directly on the work of the study team to date and has the potential to overcome significant barriers to realizing more informed, effective and equitable care.